Digital subscriber line (DSL) technology is commonly used to provide Internet related services to subscribers, such as, for example, homes and businesses (also referred to herein as users, subscribers or customers). DSL technology enables customers to utilize telephone lines (e.g., ordinary twisted-pair copper telephone lines used to provide Plain Old Telephone System (POTS) services) to connect the customer to, for example, a high data rate broadband Internet network, broadband service and broadband content.
A service provider of a DSL service can utilize information concerning the status, characteristics or performance of a telephone line or DSL equipment for a wide range of purposes such as, maintenance, service quality assurance, monitoring, trouble detection, trouble isolation, or trouble prevention. Alternatively, it may be useful to have similar information concerning the telephone line or DSL equipment prior to or during an offering, selling or provisioning of DSL service to a potential or new DSL subscriber. Example status, characteristics or performance information or data includes loop length, cable gauge(s), presence of bridged tap(s), location of bridged tap(s), lengths of bridged tap(s), noise on the line, shorts, opens, data rates, channel transfer functions, channel attenuation, signal-to-noise ratios, loop impedance, error rates, etc. Information such as that mentioned above is measured for the telephone line between the service provider's location and the subscriber's location or for the DSL equipment used to provide the DSL service to the subscriber.
DSL networks and systems measure, compute or estimate such status, characteristic or performance information using a variety of sources such as system(s), sub-system(s), method(s), server(s), protocol(s), algorithm(s) or technique(s). Such status, characteristics or performance information or data may be collected from multiple sources, which on the one hand can increase the available data, but on the other hand can complicate the analysis steps. Previous solutions to this problem either ignore the availability of data from multiple sources, or use simplistic models ignoring uncertainty factors for combining such data, which can often lead to unreliable analysis results. Also, previous solutions do not provide information with respect to the level of confidence for a certain result. As a result, these networks and systems do not measure, compute or estimate status, characteristic or performance information using two or more sources. Further, these networks and systems use simplistic models for combining data that do not estimate performance and as such, these models lead to unreliable results.